Optimizing Speech to Text Conversion and Text Summarization Using a Medical Provider Workflow Model

1. A method of processing a medical conversation comprising: converting to text, via a processor, speech signals from the medical conversation between a medical provider and a patient for a workflow of the medical provider based on a first domain model of a plurality of domain models, wherein the workflow includes a plurality of medical scenarios and the plurality of domain models are each trained for a corresponding medical scenario of the workflow, and wherein the first domain model is associated with a current medical scenario of the medical conversation; detecting, via the processor during the converting to text, one or more triggers occurring during the medical conversation, each of the one or more triggers indicating a change to a different medical scenario of the workflow within the medical conversation; in response to each of the detected one or more triggers, applying, via the processor during the converting to text, a corresponding second domain model of the plurality of domain models to the speech signals of the medical conversation pertaining to the different medical scenario indicated by the detected trigger to convert the speech signals pertaining to the different medical scenario indicated by the detected trigger from the medical conversation to text, wherein the corresponding second domain model is trained for the different medical scenario indicated by the detected trigger; and providing, via the processor, a clinical note based on the text produced from the speech signals of the medical conversation.

2. The method of claim 1 , further comprising storing the speech signals from the medical conversation, wherein the applying the corresponding second domain model further comprises: applying a plurality of second domain models to the stored speech signals based on the detected one or more triggers to convert the stored speech signals to text.

3. The method of claim 2 , wherein the detecting of the one or more triggers is based on recognizing at least one of certain words, certain phrases and certain combinations of words in the medical conversation.

4. The method of claim 1 , further comprising: learning, via the processor, one or more domain models based on previous interactions between a medical provider and a patient.

5. The method of claim 1 , wherein the plurality of domain models includes domain models at different levels of granularity for corresponding medical scenarios.

6. The method of claim 1 , wherein the plurality of domain models includes a domain model for use with a plurality of different medical scenarios.

7. The method of claim 1 , further comprising: automatically performing, via the processor, an action included in the clinical note.

8. A system for processing a medical conversation, the system comprising: at least one processor; and at least one memory connected to the at least one processor, the at least one processor being configured to: convert to text speech signals from the medical conversation between a medical provider and a patient for a workflow of the medical provider based on a first domain model of a plurality of domain models, wherein the workflow includes a plurality of medical scenarios and the plurality of domain models are each trained for a corresponding medical scenario of the workflow, and wherein the first domain model is associated with a current medical scenario of the medical conversation; detect, during the converting to text, one or more triggers occurring during the medical conversation, each of the one or more triggers indicating a change to a different medical scenario of the workflow within the medical conversation; in response to each of the detected one or more triggers, apply during the converting to text a corresponding second domain model of the plurality of domain models to the speech signals of the medical conversation pertaining to the different medical scenario indicated by the detected trigger to convert the speech signals pertaining to the different medical scenario indicated by the detected trigger from the medical conversation to text, wherein the corresponding second domain model is trained for the different in the medical scenario indicated by the detected trigger; and provide a clinical note based on the text produced from the speech signals of the medical conversation.

9. The system of claim 8 , wherein the at least one processer is further configured to: store the speech signals from the medical conversation, wherein the at least one processor being configured to apply the corresponding second domain model further comprises the at least one processor being configured to: apply a plurality of second domain models to the stored speech signals based on the detected one or more triggers to convert the stored speech signals to text.

10. The system of claim 8 , wherein the detecting of the one or more triggers is based on at least one of recognizing one or more certain words in the medical conversation and receiving signals from at least one sensor associated with a medical device.

11. The system of claim 8 , wherein the at least one processor is further configured to: learn one or more domain models based on previous interactions between a medical provider and a patient.

12. The system of claim 8 , wherein the plurality of domain models includes domain models at different levels of granularity for corresponding medical scenarios.

13. The system of claim 8 , wherein the plurality of domain models includes a domain model for use with a plurality of different medical scenarios.

14. The system of claim 8 , wherein the clinical note is arranged according to a subjective, objective, assessment and plan format.

15. A computer program product for processing a medical conversation, the computer program product comprising at least one computer readable storage medium having computer readable program code embodied therewith for execution on at least one processor of a computing device, the computer readable program code being configured to: convert to text speech signals from the medical conversation between a medical provider and a patient for a workflow of the medical provider based on a first domain model of a plurality of domain models, wherein the workflow includes a plurality of medical scenarios and the plurality of domain models are each trained for a corresponding medical scenario of the workflow, and wherein the first domain model is associated with a current medical scenario of the medical conversation; detect, during the converting to text, one or more triggers occurring during the medical conversation, each of the one or more triggers indicating a change to a different medical scenario of the workflow within the medical conversation; in response to each of the detected one or more triggers, apply during the converting to text a corresponding second domain model of the plurality of domain models to the speech signals of the medical conversation pertaining to the different medical scenario indicated by the detected trigger to convert the speech signals pertaining to the different medical scenario indicated by the detected trigger from the medical conversation to text, wherein the corresponding second domain model is trained for the different medical scenario indicated by the detected trigger; and provide a clinical note based on the text produced from the speech signals of the medical conversation.

16. The computer program product of claim 15 , wherein the computer readable program code is further configured to: store the speech signals from the medical conversation, wherein the applying the corresponding second domain model further comprises: applying a plurality of second domain models to the stored speech signals based on the detected one or more triggers to convert the stored speech signals to text.

17. The computer program product of claim 15 , wherein the detecting of the one or more triggers is based on receiving signals from at least one sensor indicating use of a medical device by the medical provider.

18. The computer program product of claim 15 , wherein the computer readable program code is further configured to: learn one or more domain models based on previous interactions between a medical provider and a patient.

19. The computer program product of claim 15 , wherein the plurality of domain models includes a domain model for use with a plurality of different medical scenarios.

20. The computer program product of claim 15 , wherein the plurality of domain models includes domain models at different levels of granularity for corresponding medical scenarios.